AIMC Topic: Neoplasms

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Constrained Tensor Factorization for Cancer Phenotyping and Mortality Prediction.

Studies in health technology and informatics
Electronic health records (EHR) enable machine learning methods like tensor factorization to extract computational phenotypes. Using Northwestern Medicine data (2000-2015), we analyzed breast, prostate, colorectal, and lung cancer cohorts to predict ...

Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis.

Studies in health technology and informatics
This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from ant...

Charting New Paths in Cancer Research: Insights from the Frontiers in Cancer Science Conference 2024.

Cancer research
The 16th annual Frontiers in Cancer Science conference convened leading experts to discuss the latest developments in cancer research. Key research themes included mechanisms of treatment resistance and innovative strategies to target resistant cance...

Endothelial metabolic zonation in the vascular network: a spatiotemporal blueprint for angiogenesis.

American journal of physiology. Heart and circulatory physiology
Angiogenesis, a cornerstone of vascular development, tissue regeneration, and tumor progression, is critically orchestrated by the metabolic behavior of endothelial cells (EC). Recent discoveries have redefined EC not as metabolically uniform entitie...

HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction.

Medical image analysis
Cancer survival prediction based on multimodal data (e.g., pathological slides, clinical records, and genomic profiles) has become increasingly prevalent in recent years. A key challenge of this task is obtaining an effective survival-specific global...

Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images.

Medical image analysis
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with pat...

Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitud...

A multi-faceted discovery strategy identifies functional antibodies binding to cysteine-rich domain 1 of hDKK1 for cancer immunotherapy via Wnt non-canonical pathway.

Oncogene
Wnt signaling is important in embryonic development and tumorigenesis. These biological effects can be exerted by the activation of the β-catenin-dependent canonical pathway or the β-catenin-independent non-canonical pathway. DKK1 is a potent inhibit...

In silico design strategies for tubulin inhibitors for the development of anticancer therapies.

Expert opinion on drug discovery
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...

Raman spectroscopy in tandem with machine learning - based decision logic methods for characterization and detection of primary precancerous and cancerous cells.

The Analyst
Early cancer detection improves patient outcomes, but most Raman spectroscopy research has focused on discriminating between normal and malignant cells, ignoring the essential precancerous stage. This study fills that gap by combining Raman spectrosc...